Teams at Helical

Own the post-training lifecycle for biological foundation models, including alignment strategy, fine-tuning, validation, experimentation, and production deployment. Build pipelines that adapt models to therapeutic contexts and client use cases, validate improvements against biological ground truth, run distributed GPU experiments, analyze results, and deliver capabilities to pharma customers. Collaborate with machine learning and biology teams, contribute to open-source tooling, and apply emerging post-training research to drug discovery workflows.

Provide scientific leadership for evaluating foundation models and applying machine learning to genomics, drug development, and translational research. Partner with research, product, and business development teams to create customer-focused workflows, mentor junior colleagues, translate complex science for varied stakeholders, and guide rapid prototype-to-insight experimentation.

Provides scientific leadership translating foundation models into drug discovery and translational research workflows. Leads model evaluation and applied biology use cases, mentors junior researchers, partners with Product and Business Development, and converts biology and machine learning research into customer deliverables. Requires expertise in computational biology, machine learning for biological data, modern ML frameworks, early drug discovery, scientific communication, and roadmap leadership.